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CHAPTER 3 Findings and Applications: Assessment Results This chapter summarizes the results of the transportation project impact assessment. Analysis of Transportation Projects For the project study area of US-101 from Mendocino County to Santa Barbara County, 68 road projects with estimated footprints totaling over 4,200 acres (Table 3.1) were analyzed. District 5 had by far the most planned projects (38 analyzed), and District 4 had the greatest estimated footprint (1,810 acres) (Table 3.2). Table 3.1. Transportation Projects by Project Type, Number Present in Each Caltrans District, and Estimated Total Footprint in Acres Project Type Number of Projects Estimated Footprint (acres) District 1 District 4 District 5 Construct expressway existing alignment 0 1 0 23.746 Construct new bridge 0 0 1 1.638 Construct retaining wall 0 0 1 21.065 High-occupancy lanes 0 2 1 253.805 Install median barrier 1 0 8 162.983 Install message signs/traffic operating systems 1 0 0 0.719 New alignment 1 1 0 1759.834 Realign curve 0 0 1 0.388 Reconstruct interchange and access ramps 6 7 13 323.617 Rehabilitate roadway 3 0 7 293.452 Roadside rest area 1 0 1 12.940 Slow-vehicles lane 0 0 3 153.091 Widen roadway 2 4 2 1228.685 Total 15 15 38 4235.964 17
Table 3.2. Number of Projects and Total Estimated Footprint by District District Number of Projects Total Estimated Footprint (acres) 1 15 1,094.40 4 15 1,810.30 5 38 1,331.27 The two new alignment projects, one in District 1 and one in District 4 (Figure 3.1), added the greatest amount of estimated footprint area, which accounts for the greater number of acres in those two districts despite District 5 having more projects. Figure 3.1. The 68 transportation projects in this study span a range of project types, each with a specified estimated footprint of impact. 18
Because each project type has a specified estimated footprint (Methodology), some projects (new alignments, road widening) had large footprint estimates, and some (installing message signs) had minimal footprint estimates. Analysis of Impacts to Species District 4 had the largest total number of acres of species habitat affected, and District 5 had the greatest number of species affected (Table 3.3). These findings are likely due to the new alignment planned in District 4. District 5 overall had a greater number of listed species than the other two districts. Table 3.3. Number of Species and Estimated Number of Acres of Habitat Affected for 2- and 4-Mile Buffered Areas by District Variable District 1 District 4 District 5 Number of species affected 3 11 14 Acres of affected habitat (2-mile buffer) 13.59 364.91 287.91 Acres of affected habitat (4-mile buffer) 47.31 876.2 483.26 The tricolored blackbird had likely habitat present in all three districts and had the highest number of acres of estimated habitat affected, 54.4 and 402.5 acres for the 2-mile and 4-mile buffered areas, respectively (Table 3.4). The San Joaquin kit fox and California tiger salamander had likely habitat present within estimated transportation project footprints in two districts and had the second- and third-highest estimated acreage. Many of the estimated areas of impact to species are found within multiple projects and, in some cases, in multiple counties or districts (Figure 3.2). This situation again highlights an area of opportunity for bundling compensatory mitigation funds from multiple projects to purchase a more ecologically significant parcel. 19
Table 3.4. Species Analyzed and Estimated Number of Acres of Habitat Affected from Project Footprints for 2- and 4-mile Buffered Areas in Caltrans Districts 1, 4, and 5 Species Common Name 2-Mile Buffer Area (acres) 4-Mile Buffer Area (acres) District(s) Found Burrowing owl 42.9 168.0 4, 5 California black rail 5.1 5.7 4 California clapper rail 13.4 13.6 4 California least tern 2.4 2.4 4 California red-legged frog 113.8 157.8 4, 5 California tiger salamander 287.1 337.9 4, 5 Fisher 13.3 43.9 1 Least Bell's vireo 14.6 17.5 4, 5 La Graciosa thistle 29.9 76.0 5 Lompoc yerba santa 0.0 1.0 5 Milo Baker's lupine 0.3 0.4 1 Monterey spineflower 0.0 35.5 5 Pismo Clarkia 0.0 0.1 5 Salt marsh birdâs-beak 0.0 1.0 5 Salt marsh harvest mouse 0.0 1.7 4 San Joaquin kit fox 74.3 188.5 4, 5 Showy rancheria clover 0.0 1.4 4 Townsendâs big-eared bat 5.8 16.0 5 Tricolored blackbird 54.4 402.5 1, 4, 5 Vernal pool fairy shrimp 5.2 5.2 5 Yadonâs rein orchid 3.8 8.7 5 Total 666.4 1484.7 20
Figure 3.2. Two examples of estimated impacts to species: (a) likely fisher habitat, found in three projects in District 1 (two shown); and (b) likely burrowing owl habitat, found in seven projects in Districts 4 and 5 (two shown). Analysis of Urban Impacts The urban impacts analysis was performed only in District 4, the San Francisco Bay Area, which is a highly urbanized region. Using the parcel layer allowed for the comparison of land use types within the urbanized area, including agricultural lands. The parcel map does not include rights-of-way for transportation corridors, but it does include facilities that are related to transportation activities, such as train stations and airports. Farmland was the most heavily affected land use type in District 4, followed by commercial and industrial types (Table 3.5). Satellite imagery in Figure 3.3 shows two areas within District 4 where different land use types can be seen. Census data could be added to further explore the demographic characteristics within the distinct land use classes. (a) (b) 21
Table 3.5. Initial Estimate of Urban Impacts in District 4 (San Francisco Bay Area), Including Farmland, from the 15 Planned Transportation Projects Figure 3.3. Map showing two projects in Santa Clara County: (a) road widening affecting farmland and residential areas and (b) new lane construction affecting an urbanized area. Analysis of Aquatic Impacts Discussions with a Caltrans fish biologist revealed that although impacts from road construction projects may go far beyond the immediate vicinity of where a road project crosses a stream, the mitigation for fish impacts is typically based on an estimate of the number of fish killed. This number can be considerable, particularly for fry. It was not possible to project such numbers; therefore, for a projection of impacts, the number of Land Use Type Number of Acres Farmland 1,031.4 Residential 32.4 Commercial/Industrial 98.6 Vacant 36.8 Transportation 53.5 Other/Miscellaneous 77.1 (a) (b) (a) 22
times road projects crossed streams was used to develop an accounting of impacts to streams. There were 44 stream crossings associated with the transportation projects in the Bay Area and central coast areas: 16 in District 4 and 28 in District 5 (Table 3.6). Table 3.6. Assessment of Stream Crossings in Districts 4 and 5 by Transportation Project Type District Project Type County Number of Stream Crossings 4 High-occupancy lane Santa Clara 1 4 Widen roadway Santa Clara and San Benito 4 4 Widen roadway Santa Clara and San Benito 11 5 Construct new bridge San Luis Obispo 1 5 Construct retaining wall Santa Barbara 1 5 Install median barrier San Luis Obispo 1 5 Install median barrier Santa Barbara 1 5 Rehabilitate roadway San Luis Obispo 2 5 Rehabilitate roadway Monterey and San Luis Obispo 1 5 Rehabilitate roadway San Luis Obispo 3 5 Rehabilitate roadway Santa Barbara 5 5 Rehabilitate roadway Santa Barbara 1 5 Rehabilitate roadway Santa Barbara 1 5 Roadside rest area Santa Barbara 1 5 US-101 bridge rail replacements Santa Barbara 7 5 Widen roadway Santa Clara and San Benito 3 Total 44 Some wetland and aquatic habitats, including emergent marsh, floodplain riparian, freshwater marsh, riparian forest, and wetlands, were also likely to be affected by the planned transportation projects in this study (Table 3.7). The research team assessed these impacts by using a national data set (NWI), a statewide data set (CALVEG), and a localized data set for District 4 (CLN). The national data set indicated more wetland impacts consistently for the same projects, especially in Districts 1 and 4. 23
Table 3.7. Comparison of Aquatic Impacts from Different Wetland and Vegetation Layers Dataset Wetland Type District 1 District 4 District 5 Acres Projects Acres Projects Acres Projects CALVEG Lacustrine 1.74 2 2.13 5 Montane riparian 2.82 3 Riverine 0.33 1 0.19 1 Saline emergent wetland 1.46 1 Valley foothill riparian 28.37 9 CLN Coastal salt marsh/coastal brackish marsh 1.73 1 Permanent freshwater marsh 0.73 1 Valley oak forest/woodland 5.68 1 Water 3.15 2 NWI Estuarine and marine deep water 1.14 2 0.09 1 Estuarine and marine wetland 1.05 1 1.15 1 Freshwater emergent wetland 4.45 1 5.50 5 8.80 9 Freshwater forested/shrub wetland 4.22 3 28.99 13 Freshwater pond 14.10 1 1.93 3 0.01 1 Riverine 1.49 2 15.49 4 13.06 17 Total Aquatic Area (CALVEG and CLN) 4.89 6 11.28 5 32.15 6 Total Aquatic Area (NWI) 20.04 4 29.33 18 52.10 42 This discrepancy could be due to a difference in spatial resolution. The CLN layer is a raster format that was created using a 30-meter cell size, which creates the distinctive pattern of patchy triangles shown in Figure 3.4. CALVEG is in vector format, but it also has the jagged edges characteristic of a raster, at a 5-meter cell size, which is likely due to the remote sensing data sets from which it is derived. The NWI data set, in contrast, was originally created as a vector file by using U.S. Geological Survey topographic maps as the base maps; it generally has smoother edges, as shown in Figure 3.4. However, there are possible other reasons for the discrepancy, such as different mapping dates. 24
Figure 3.4. A section of Highway 101 in District 4 showing aquatic impacts from the NWI (freshwater emergent wetland) and CLN (water, permanent freshwater marsh) layers. Nonaquatic areas in the CLN layer that overlap are classified as urban. The different classes used by each data layer could also account for the differences in wetland area calculated in the analysis. For example, CALVEG has two riparian classes, montane riparian and valley foothill riparian, but the NWI classification has a broader category of freshwater forested/shrub wetland. It is therefore difficult to directly compare some wetland categories. From a regulatory perspective, it may be necessary to include both maps, as one helps guide state regulatory considerations and the other, federal ones. 25
